A Soft Computing Approach for Learning to Aggregate Rankings

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Javier Alvaro Vargas Munoz and 
                 Ricardo {da Silva Torres} and Marcos Andre Goncalves",
  title =        "A Soft Computing Approach for Learning to Aggregate
  booktitle =    "Proceedings of the 24th {ACM} International on
                 Conference on Information and Knowledge Management,
  year =         "2015",
  pages =        "83--92",
  address =      "Melbourne, Australia",
  month =        oct # " 19 - 23",
  keywords =     "genetic algorithms, genetic programming",
  bibsource =    "dblp computer science bibliography, http://dblp.org",
  biburl =       "http://dblp.uni-trier.de/rec/bib/conf/cikm/MunozTG15",
  timestamp =    "Thu, 12 Nov 2015 16:33:35 +0100",
  URL =          "http://doi.acm.org/10.1145/2806416.2806478",
  DOI =          "doi:10.1145/2806416.2806478",
  abstract =     "This paper presents an approach to combine rank
                 aggregation techniques using a soft computing technique
                 -- Genetic Programming -- in order to improve the
                 results in Information Retrieval tasks. Previous work
                 shows that by combining rank aggregation techniques in
                 an agglomerative way, it is possible to get better
                 results than with individual methods. However, these
                 works either combine only a small set of lists or are
                 performed in a completely ad-hoc way. Therefore, given
                 a set of ranked lists and a set of rank aggregation
                 techniques, we propose to use a supervised genetic
                 programming approach to search combinations of them
                 that maximize effectiveness in large search spaces.
                 Experimental results conducted using four datasets with
                 different properties show that our proposed approach
                 reaches top performance in most datasets. Moreover,
                 this cross-dataset performance is not matched by any
                 other baseline among the many we experiment with, some
                 being the state-of-the-art in learning-to-rank and in
                 the supervised rank aggregation tasks. We also show
                 that our proposed framework is very efficient,
                 flexible, and scalable.",

Genetic Programming entries for Javier Alvaro Vargas Munoz Ricardo da Silva Torres Marcos Andre Goncalves